{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n",
      "sys.version_info(major=3, minor=6, micro=9, releaselevel='final', serial=0)\n",
      "matplotlib 3.1.3\n",
      "numpy 1.18.1\n",
      "pandas 1.0.1\n",
      "sklearn 0.22.1\n",
      "tensorflow 2.1.0\n",
      "tensorflow_core.python.keras.api._v2.keras 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "print(tf.__version__)\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, tf, keras:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "'''\n",
    "课程开始的地方（梦想开始的地方）\n",
    "1. 获取图片分类数据\n",
    "2. 使用plt显示图像信息\n",
    "3. 将多张图片填充在一起显示大图片\n",
    "4. 训练模型\n",
    "5. history\n",
    "6. 验证模型 evaluate\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 28, 28) (5000,)\n",
      "(55000, 28, 28) (55000,)\n",
      "(10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()\n",
    "x_valid, x_train = x_train_all[:5000], x_train_all[5000:]\n",
    "y_valid, y_train = y_train_all[:5000], y_train_all[5000:]\n",
    "\n",
    "print(x_valid.shape, y_valid.shape)\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_single_image(img_arr):\n",
    "    plt.imshow(img_arr, cmap=\"binary\")\n",
    "    plt.show()\n",
    "\n",
    "show_single_image(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x345.6 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_imgs(n_rows, n_cols, x_data, y_data, class_names):\n",
    "    ## 使用断言\n",
    "    assert len(x_data) == len(y_data)\n",
    "    assert n_rows * n_cols < len(x_data)\n",
    "    plt.figure(figsize = (n_cols * 1.4, n_rows * 1.6))\n",
    "    for row in range(n_rows):\n",
    "        for col in range(n_cols):\n",
    "            index = n_cols * row + col \n",
    "            plt.subplot(n_rows, n_cols, index+1)\n",
    "            plt.imshow(x_data[index], cmap=\"binary\",\n",
    "                       interpolation = 'nearest')\n",
    "            plt.axis('off')\n",
    "            plt.title(class_names[y_data[index]])\n",
    "    plt.show()\n",
    "\n",
    "class_names = ['T-shirt', 'Trouser', 'Pullover', 'Dress',\n",
    "               'Coat', 'Sandal', 'Shirt', 'Sneaker',\n",
    "               'Bag', 'Ankle boot']\n",
    "show_imgs(3, 5, x_train, y_train, class_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tf.keras.models.Sequential()\n",
    "\n",
    "\"\"\"\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
    "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
    "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "\"\"\"\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    keras.layers.Dense(300, activation='relu'),\n",
    "    keras.layers.Dense(100, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "# relu: y = max(0, x)\n",
    "# softmax: 将向量变成概率分布. x = [x1, x2, x3], \n",
    "#          y = [e^x1/sum, e^x2/sum, e^x3/sum], sum = e^x1 + e^x2 + e^x3\n",
    "\n",
    "# reason for sparse: y->index. y->one_hot->[] \n",
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer = \"sgd\",\n",
    "              metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.layers.core.Flatten at 0x12d20ed68>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x12f5d45f8>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x12f65c7f0>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x12f6250b8>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 查看model layer\n",
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 300)               235500    \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 100)               30100     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 266,610\n",
      "Trainable params: 266,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [None, 784] * W + b -> [None, 300] W.shape [784, 300], b = [300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 55000 samples, validate on 5000 samples\n",
      "Epoch 1/10\n",
      "55000/55000 [==============================] - 6s 113us/sample - loss: 1.9139 - accuracy: 0.7326 - val_loss: 0.6136 - val_accuracy: 0.7792\n",
      "Epoch 2/10\n",
      "55000/55000 [==============================] - 6s 111us/sample - loss: 0.5238 - accuracy: 0.8138 - val_loss: 0.5287 - val_accuracy: 0.8222\n",
      "Epoch 3/10\n",
      "55000/55000 [==============================] - 7s 133us/sample - loss: 0.4594 - accuracy: 0.8328 - val_loss: 0.5024 - val_accuracy: 0.8256\n",
      "Epoch 4/10\n",
      "55000/55000 [==============================] - 7s 122us/sample - loss: 0.4248 - accuracy: 0.8452 - val_loss: 0.4976 - val_accuracy: 0.8286\n",
      "Epoch 5/10\n",
      "55000/55000 [==============================] - 6s 110us/sample - loss: 0.4006 - accuracy: 0.8526 - val_loss: 0.4797 - val_accuracy: 0.8378\n",
      "Epoch 6/10\n",
      "55000/55000 [==============================] - 6s 110us/sample - loss: 0.3833 - accuracy: 0.8591 - val_loss: 0.4424 - val_accuracy: 0.8486\n",
      "Epoch 7/10\n",
      "55000/55000 [==============================] - 6s 118us/sample - loss: 0.3687 - accuracy: 0.8642 - val_loss: 0.4254 - val_accuracy: 0.8558\n",
      "Epoch 8/10\n",
      "55000/55000 [==============================] - 6s 114us/sample - loss: 0.3568 - accuracy: 0.8681 - val_loss: 0.4346 - val_accuracy: 0.8522\n",
      "Epoch 9/10\n",
      "55000/55000 [==============================] - 6s 115us/sample - loss: 0.3458 - accuracy: 0.8727 - val_loss: 0.4304 - val_accuracy: 0.8542\n",
      "Epoch 10/10\n",
      "55000/55000 [==============================] - 6s 114us/sample - loss: 0.3366 - accuracy: 0.8759 - val_loss: 0.4235 - val_accuracy: 0.8570\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=10,\n",
    "                    validation_data=(x_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensorflow.python.keras.callbacks.History"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [4.599787224023992,\n",
       "  0.7365305235429244,\n",
       "  0.6529739934054288,\n",
       "  0.5826076885786924,\n",
       "  0.5450057860287753,\n",
       "  0.5157407429044897,\n",
       "  0.4949760743748058,\n",
       "  0.4669956823869185,\n",
       "  0.4407323715166612,\n",
       "  0.4275606528585607],\n",
       " 'accuracy': [0.6249273,\n",
       "  0.7049818,\n",
       "  0.7367091,\n",
       "  0.7716182,\n",
       "  0.7889636,\n",
       "  0.80014545,\n",
       "  0.80945456,\n",
       "  0.82485455,\n",
       "  0.8361818,\n",
       "  0.8401818],\n",
       " 'val_loss': [0.7757820220947266,\n",
       "  0.6484994589209556,\n",
       "  0.6377250983238221,\n",
       "  0.5946815532684326,\n",
       "  0.5697053789138794,\n",
       "  0.5346372863769532,\n",
       "  0.5146976159095764,\n",
       "  0.5634896508932113,\n",
       "  0.4952246562957764,\n",
       "  0.4846856918811798],\n",
       " 'val_accuracy': [0.7062,\n",
       "  0.7338,\n",
       "  0.745,\n",
       "  0.774,\n",
       "  0.7828,\n",
       "  0.8024,\n",
       "  0.8114,\n",
       "  0.8094,\n",
       "  0.8264,\n",
       "  0.8342]}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curves(history):\n",
    "    pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
    "    plt.grid(True)\n",
    "    plt.gca().set_ylim(0, 1)\n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curves(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 96us/sample - loss: 0.4376 - accuracy: 0.8532\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.43761702871322633, 0.8532]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
